Recently, because of the large capacity and lower prices of memory devices, large quantities of data are stored in a database. In such a database, the data, organized by units of predetermined periods such as times, day, week, or month, are stored for relative long time. In this case, information such as a sales result is stored in this data base. By analysing the information, a tendency related with sales (consumer's purchase) is extracted for a marketing or sales strategy.
FIG. 10 shows one example of sales data of a product gathered by a retail store. FIGS. 10A, 10B, 10C respectively show sales trends of product X and product Y from April to October in shops A, B, C. The data in FIG. 10 is plotted as a graph and easily analyzed by sight. In this case, the following three aspects are obtained as analysis result.
(1) In shop A, sales of product X grow from April to June and sales of product Y grow from June to August.
(2) In shop B, sales of product X grow from May to July and sales of product Y grow from July to September.
(3) In shop C, sales of product X grow from April to June and sales of product Y grow from June to August.
As a proposition estimated from above analysis, a rule such as "If sales of product X grow, sales of product Y also grow after two months" is considered.
In this way, an extraction of the rule including the time series is very important for early detection of a change sign on trade activity of the shop. In order to obtain the rule, the sales of each product are represented as a graph. A specialist decides the association rule between the time series data by watching the graph. However, if a large number of products are treated in each shop, the sales data for a long period is analysed, and the number of shops becomes large, the specialist can not easily analyse the sales data by himself.
As a method to analyse a similarity of shape of a time difference between the time series, a combination of two time series data is created. In this case, whenever one time series data is shifted along a direction of time axis, statistical analysis such as regression analysis is executed to find the association rule between the two time series data. However, the calculation burden increases in proportion to the product of the number of combinations of series number and the time length of the two time series data. Accordingly, this method is not suitable for the time series analysis including a large series number and a long period.
Furthermore, in case of analysing the sales data, the analysis result is often extracted due to not association between two time series data but to association between one time series data and a predetermined time. For example, if sales of cake rapidly grow toward December 24th and sales of rice cake rapidly grow after December 25th for all shops, a rule such as "If sales of cake grow, sales of rice cake also grow after one week" is mistakenly extracted. In this way, as for sales object related with predetermined time, a rule representing dependency between the predetermined time and the time series data is desirably extracted as "The cake sells well toward Christmas and the rice cake sells well toward New Year's day."
As mentioned-above, when extracting the association rule from large scale time series data, the specialist can not easily analyse the time series data by sight alone. Furthermore, in case of the statistical analysis, the processing time increases in proportion to a product of a number of combination of series number and a quantity of the time series data. Therefore, the statistical analysis is not suitable for the time series analysis including a large series number and a long period.